ガウス過程支援による動的制約を考慮した自動運転車のモデル予測制御
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
人間の運転行動をガウス過程で学習・予測し、その不確かさを利用して安全性を確保する自動運転のモデル予測制御手法を提案。複数の意図を確率的に扱うマルチモード制御で実現可能性と制約充足を高確率で保証する。
著者: Johanna Bethge, Maik Pfefferkorn, Alexander Rose, Jan Peters, Rolf Findeisen
分類: eess.SY, cs.LG, cs.RO, cs.SY, math.OC
原文アブストラクト
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. A multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables repeated feasibility and probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.
